{"id":"https://openalex.org/W2995785927","doi":"https://doi.org/10.1109/tetc.2019.2958946","title":"DiabDeep: Pervasive Diabetes Diagnosis Based on Wearable Medical Sensors and Efficient Neural Networks","display_name":"DiabDeep: Pervasive Diabetes Diagnosis Based on Wearable Medical Sensors and Efficient Neural Networks","publication_year":2019,"publication_date":"2019-12-17","ids":{"openalex":"https://openalex.org/W2995785927","doi":"https://doi.org/10.1109/tetc.2019.2958946","mag":"2995785927"},"language":"en","primary_location":{"id":"doi:10.1109/tetc.2019.2958946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetc.2019.2958946","pdf_url":null,"source":{"id":"https://openalex.org/S2496326734","display_name":"IEEE Transactions on Emerging Topics in Computing","issn_l":"2168-6750","issn":["2168-6750","2376-4562"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002444694","display_name":"Hongxu Yin","orcid":"https://orcid.org/0000-0002-6481-6389"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongxu Yin","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-6481-6389","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065379026","display_name":"Bilal Mukadam","orcid":null},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bilal Mukadam","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080137972","display_name":"Xiaoliang Dai","orcid":"https://orcid.org/0000-0003-3098-2714"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoliang Dai","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0003-3098-2714","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086131079","display_name":"Niraj K. Jha","orcid":"https://orcid.org/0000-0002-1539-0369"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Niraj K. Jha","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-1539-0369","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":9.6544,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.9800804,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"9","issue":"3","first_page":"1139","last_page":"1150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11446","display_name":"Mobile Health and mHealth Applications","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11446","display_name":"Mobile Health and mHealth Applications","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8722084164619446},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.6751834154129028},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.6202247738838196},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5701786875724792},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5408173203468323},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.5368355512619019},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5161384344100952},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.4859551191329956},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4800587594509125},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.46678459644317627},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43815168738365173},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4309070110321045},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4255684018135071},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3424970209598541},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3010043501853943},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.1610928773880005},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.1012071967124939}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8722084164619446},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.6751834154129028},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.6202247738838196},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5701786875724792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5408173203468323},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.5368355512619019},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5161384344100952},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.4859551191329956},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4800587594509125},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.46678459644317627},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43815168738365173},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4309070110321045},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4255684018135071},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3424970209598541},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3010043501853943},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.1610928773880005},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.1012071967124939},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tetc.2019.2958946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetc.2019.2958946","pdf_url":null,"source":{"id":"https://openalex.org/S2496326734","display_name":"IEEE Transactions on Emerging Topics in Computing","issn_l":"2168-6750","issn":["2168-6750","2376-4562"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2351308602","display_name":null,"funder_award_id":"CNS-1617640","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W1690739335","https://openalex.org/W1821462560","https://openalex.org/W1996773027","https://openalex.org/W2019985891","https://openalex.org/W2061326496","https://openalex.org/W2101234009","https://openalex.org/W2163605009","https://openalex.org/W2338167986","https://openalex.org/W2346808375","https://openalex.org/W2557738935","https://openalex.org/W2560017826","https://openalex.org/W2560030487","https://openalex.org/W2569214105","https://openalex.org/W2585720638","https://openalex.org/W2620740922","https://openalex.org/W2739879705","https://openalex.org/W2754526845","https://openalex.org/W2785925437","https://openalex.org/W2794664194","https://openalex.org/W2798977369","https://openalex.org/W2805707465","https://openalex.org/W2883780447","https://openalex.org/W2887783173","https://openalex.org/W2899641901","https://openalex.org/W2899771611","https://openalex.org/W2910476127","https://openalex.org/W2938315635","https://openalex.org/W2944978536","https://openalex.org/W2948833786","https://openalex.org/W2950722229","https://openalex.org/W2962883174","https://openalex.org/W2963163009","https://openalex.org/W2963319203","https://openalex.org/W2963424132","https://openalex.org/W2963674932","https://openalex.org/W2963844898","https://openalex.org/W2963847166","https://openalex.org/W2964037754","https://openalex.org/W2964118293","https://openalex.org/W2967733054","https://openalex.org/W2980137827","https://openalex.org/W2991232789","https://openalex.org/W3092065440","https://openalex.org/W3098718496","https://openalex.org/W3122152400","https://openalex.org/W4233285527","https://openalex.org/W4285719527","https://openalex.org/W4288365915","https://openalex.org/W4297689207","https://openalex.org/W6637551013","https://openalex.org/W6638523607","https://openalex.org/W6675354045","https://openalex.org/W6684191040","https://openalex.org/W6730047919","https://openalex.org/W6736780897","https://openalex.org/W6744208968","https://openalex.org/W6748098738","https://openalex.org/W6750362633","https://openalex.org/W6751791611","https://openalex.org/W6753767121","https://openalex.org/W6756040250","https://openalex.org/W6761306528","https://openalex.org/W6762862114","https://openalex.org/W6947966326"],"related_works":["https://openalex.org/W3090300519","https://openalex.org/W2514492205","https://openalex.org/W4250401876","https://openalex.org/W2943851981","https://openalex.org/W2095299560","https://openalex.org/W2907667791","https://openalex.org/W3047461507","https://openalex.org/W3126390843","https://openalex.org/W4245880644","https://openalex.org/W2999186374"],"abstract_inverted_index":{"Diabetes":[0],"impacts":[1],"the":[2,11,30,117,135,168,181,212,246,325,336],"quality":[3],"of":[4,6,8,34,214,248,254],"life":[5],"millions":[7],"people":[9],"around":[10],"globe.":[12],"However,":[13,65],"diabetes":[14,113],"diagnosis":[15,67],"is":[16],"still":[17],"an":[18,130,145],"arduous":[19],"process,":[20],"given":[21],"that":[22,101],"this":[23,50],"disease":[24,66],"develops":[25],"and":[26,39,61,72,87,121,140,157,164,201,226,237,242,279,293,306,315,343],"gets":[27],"treated":[28],"outside":[29],"clinic.":[31],"The":[32],"emergence":[33],"wearable":[35],"medical":[36],"sensors":[37],"(WMSs)":[38],"machine":[40],"learning":[41],"points":[42],"to":[43,48,59,83,151,176,195,230,233,332],"a":[44,54,97,138,149,153,187,207,218,251,267,280],"potential":[45],"way":[46],"forward":[47],"address":[49,92],"challenge.":[51],"WMSs":[52,110],"enable":[53,231],"continuous,":[55],"yet":[56,345],"user-transparent,":[57],"mechanism":[58],"collect":[60],"analyze":[62],"physiological":[63],"signals.":[64],"based":[68,159,192],"on":[69,76,124,134,144,160,199],"WMS":[70,125],"data":[71,255],"its":[73],"effective":[74],"deployment":[75],"resource-constrained":[77],"edge":[78,146,183],"devices":[79],"remain":[80],"challenging":[81],"due":[82],"inefficient":[84],"feature":[85,118],"extraction":[86,119],"vast":[88],"computation":[89,200],"cost.":[90],"To":[91],"these":[93],"problems,":[94],"we":[95,171,185,265],"propose":[96],"framework":[98],"called":[99],"DiabDeep":[100,115,249],"combines":[102],"efficient":[103,142],"neural":[104],"networks":[105],"(called":[106],"DiabNNs)":[107],"with":[108,304],"off-the-shelf":[109],"for":[111],"pervasive":[112,342],"diagnosis.":[114],"bypasses":[116],"stage":[120],"acts":[122],"directly":[123],"data.":[126],"It":[127],"enables":[128],"both":[129,235],"(i)":[131],"accurate":[132],"inference":[133,143],"server,":[136,170],"e.g.,":[137,148],"desktop,":[139],"(ii)":[141],"device,":[147,184],"smartphone,":[150],"obtain":[152],"balance":[154],"between":[155],"accuracy":[156,209,241,272,285],"efficiency":[158],"varying":[161],"resource":[162],"budgets":[163],"design":[165],"goals.":[166],"On":[167,180],"resource-rich":[169],"stack":[172],"sparsely":[173],"connected":[174],"layers":[175],"deliver":[177],"high":[178],"accuracy.":[179],"resource-scarce":[182],"use":[186],"hidden-layer":[188],"long":[189],"short-term":[190],"memory":[191],"recurrent":[193],"layer":[194],"substantially":[196],"cut":[197],"down":[198],"storage":[202],"costs":[203],"while":[204,239,323],"incurring":[205],"only":[206],"minor":[208],"loss.":[210],"At":[211],"core":[213],"our":[215],"system":[216,337],"lies":[217],"grow-and-prune":[219],"training":[220],"flow:":[221],"it":[222],"leverages":[223],"gradient-based":[224],"growth":[225],"magnitude-based":[227],"pruning":[228],"algorithms":[229],"DiabNNs":[232,319],"learn":[234],"weights":[236],"connections,":[238],"improving":[240],"efficiency.":[243],"We":[244],"demonstrate":[245],"effectiveness":[247],"through":[250],"detailed":[252],"analysis":[253],"collected":[256],"from":[257],"52":[258],"participants.":[259],"For":[260],"server":[261],"(edge)":[262],"side":[263],"inference,":[264],"achieve":[266,320],"96.3":[268],"percent":[269,282],"(95.3":[270],"percent)":[271,284],"in":[273,286],"classifying":[274],"diabetics":[275],"against":[276],"healthy":[277,294],"individuals,":[278],"95.7":[281],"(94.6":[283],"distinguishing":[287],"among":[288],"type-1":[289],"diabetic,":[290,292],"type-2":[291],"individuals.":[295],"Against":[296],"conventional":[297],"baselines,":[298],"such":[299],"as":[300,341],"support":[301],"vector":[302],"machines":[303],"linear":[305,316],"radial":[307],"basis":[308],"function":[309],"kernels,":[310],"k-nearest":[311],"neighbor,":[312],"random":[313],"forest,":[314],"ridge":[317],"classifiers,":[318],"higher":[321],"accuracy,":[322],"reducing":[324],"model":[326],"size":[327],"(floating-point":[328],"operations)":[329],"by":[330],"up":[331],"454.5\u00d7":[333],"(8.9\u00d7).":[334],"Therefore,":[335],"can":[338],"be":[339],"viewed":[340],"efficient,":[344],"very":[346],"accurate.":[347]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}